Process modeling in the pharmaceutical industry using the discrete element method

Detailed accounts of segregation mechanisms and the evolution of pharmaceutical blend segregation analysis: A review

Segregation refers to the separation of components in a powder mixture, resulting in potential issues related to concentration inhomogeneity. Any well-mixed blend that undergoes secondary processing is inherently susceptible to segregation which, if unmitigated, will lead to active compound concentration variance and poorer product quality. The consequences range from adverse financial impact to manufacturers with product failures to the detrimental health effects to product users. Hence, the topic of segregation is of paramount importance to the industry, requiring it to be dissected and scrutinized intensively by scientists worldwide. This review provides a well-crafted theoretical framework designed to understand the common segregation mechanisms that manufacturing facilities face, followed by the efforts to gauge the degree of segregation. To minimize segregation in blends, various approaches - mathematical modeling, empirical experiments, and empirical methods with modeling consideration - have been utilized in segregation research and are covered in this review. The past segregation studies from many fields are discussed, with particular emphasis on pharmaceuticals. The review also discusses the evolution and advances in mixing technology and storage systems implemented by the pharmaceutical industry to prevent segregation. In the conclusion, the authors articulated their perspectives on potential mitigation measures, including suggestions for improvements and future studies.

Investigating screw-agitator speed ratio impact on feeding performance in pharmaceutical manufacturing using discrete element method

In continuous powder handling processes, precise and consistent feeding is crucial for ensuring the quality of the final product. The intermixing effect caused by agitators, which alters the powder's bulk density, flow rate, and flow patterns, plays a significant role in this process, yet it is often overlooked. This study combines discrete element method (DEM) modeling and experiments using a commercial-scale feeder to propose a Digital Twin (DT) framework. The DEM model accurately captures key flow features, such as bypass trajectories, stagnant zones, and preferential flow patterns, while providing quantitative predictions for the feed factor and zones prone to material accumulation. Scenario analysis is performed to identify the most favorable operating ranges of the screw-agitator ratio and screw speed, considering the cohesive properties of the powder. The study demonstrates that powders with poor flow characteristics require tighter operational constraints, as the screw-agitator ratio is susceptible to variations in mass feed rate. This contribution highlights the importance of selecting an appropriate screw-agitator ratio instead of maintaining a fixed value. Properly choosing this ratio helps determine an optimal operation window, which aims to achieve a minimum agitation level needed to induce unhindered flow and reduce variability in the mass flow rate.

A Blend Consisting of Agaran from Seaweed Gracilaria birdiae and Chromium Picolinate Is a Better Antioxidant Agent than These Two Compounds Alone

A blend refers to the combination of two or more components to achieve properties that are superior to those found in the individual products used for their production. Gracilaria birdiae agaran (SPGb) and chromium picolinate (ChrPic) are both antioxidant agents. However, there is no documentation of blends that incorporate agarans and ChrPic. Hence, the objective of this study was to generate blends containing SPGb and ChrPic that exhibit enhanced antioxidant activity compared to SPGb or ChrPic alone. ChrPic was commercially acquired, while SPGb was extracted from the seaweed. Five blends (B1; B2; B3; B4; B5) were produced, and tests indicated B5 as the best antioxidant blend. B5 was not cytotoxic or genotoxic. H₂O₂ (0.6 mM) induced toxicity in fibroblasts (3T3), and this effect was abolished by B5 (0.05 mg·mL^-1); neither ChrPic nor SPGb showed this effect. The cells also showed no signs of toxicity when exposed to H₂O₂ after being incubated with B5 and ChrPic for 24 h. In another experiment, cells were incubated with H₂O₂ and later exposed to SPGb, ChrPic, or B5. Again, SPGb was not effective, while cells exposed to ChrPic and B5 reduced MTT by 100%. The data demonstrated that B5 has activity superior to SPGb and ChrPic and points to B5 as a product to be used in future in vivo tests to confirm its antioxidant action. It may also be indicated as a possible nutraceutical agent.

Advances in numerical simulation of unit operations for tablet preparation

Recently, there has been an increase in the use of numerical simulation technology in pharmaceutical preparation processes. Numerical simulation can contribute to a better understanding of processes, reduce experimental costs, optimize preparation processes, and improve product quality. The intermediate material of most dosage forms is powder or granules, especially in the case of solid preparations. The macroscopic behavior of particle materials is controlled by the interactions of individual particles with each other and surrounding fluids. Therefore, it is very important to analyze and control the microscopic details of the preparation process for solid preparations. Since tablets are one of the most widely used oral solid preparations, and the preparation process is relatively complex and involves numerous units of operation, it is especially important to analyze and control the tablet production process. The present paper discusses recent advances in numerical simulation technology for the preparation of tablets, including drying, mixing, granulation, tableting, and coating. It covers computational fluid dynamics (CFD), discrete element method (DEM), population balance model (PBM), finite element method (FEM), Lattice-Boltzmann model (LBM), and Monte Carlo model (MC). The application and deficiencies of these models in tablet preparation unit operations are discussed. Furthermore, the paper provides a systematic reference for the control and analysis of the tablet preparation process and provides insight into the future direction of numerical simulation technology in the pharmaceutical industry.

Role of Crystal Disorder and Mechanoactivation in Solid-State Stability of Pharmaceuticals

Common energy-intensive processes applied in oral solid dosage development, such as milling, sieving, blending, compaction, etc. generate particles with surface and bulk crystal disorder. An intriguing aspect of the generated crystal disorder is its evolution and repercussion on the physical- and chemical stabilities of drugs. In this review, we firstly examine the existing literature on crystal disorder and its implications on solid-state stability of pharmaceuticals. Secondly, we discuss the key aspects related to the generation and evolution of crystal disorder, dynamics of the disordered/amorphous phase, analytical techniques to measure/quantify them, and approaches to model the disordering propensity from first principles. The main objective of this compilation is to provide special impetus to predict or model the chemical degradation(s) resulting from processing-induced manifestation in bulk solid manufacturing. Finally, a generic workflow is proposed that can be useful to investigate the relevance of crystal disorder on the degradation of pharmaceuticals during stability studies. The present review will cater to the requirements for developing physically- and chemically stable drugs, thereby enabling early and rational decision-making during candidate screening and in assessing degradation risks associated with formulations and processing.

Modelling the Evolution of Pore Structure during the Disintegration of Pharmaceutical Tablets

Pharmaceutical tablet disintegration is a critical process for dissolving and enabling the absorption of the drug substance into the blood stream. The tablet disintegration process consists of multiple connected and interdependent mechanisms: liquid penetration, swelling, dissolution, and break-up. One key dependence is the dynamic change of the pore space in a tablet caused by the swelling of particles while the tablet takes up liquid. This study analysed the changes in the pore structure during disintegration by coupling the discrete element method (DEM) with a single-particle swelling model and experimental liquid penetration data from terahertz-pulsed imaging (TPI). The coupled model is demonstrated and validated for pure microcrystalline cellulose (MCC) tablets across three porosities (10, 15, and 22%) and MCC with three different concentrations of croscarmellose sodium (CCS) (2, 5, and 8% w/w). The model was validated using experimental tablet swelling from TPI. The model captured the difference in the swelling behaviour of tablets with different porosities and formulations well. Both the experimental and modelling results showed that the swelling was lowest (i.e., time to reach the maximum normalised swelling capacity) for tablets with the highest CCS concentration, cCCS = 8%. The simulations revealed that this was caused by the closure of the pores in both the wetted volume and dry volume of the tablet. The closure of the pores hinders the liquid from accessing other particles and slows down the overall swelling process. This study provides new insights into the changes in the pore space during disintegration, which is crucial to better understand the impact of porosity and formulations on the performance of tablets.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

Numerical Investigation of Triaxial Shear Behaviors of Cemented Sands with Different Sampling Conditions Using Discrete Element Method

In cemented sand, the influences of the sampling factors (i.e., the curing time, cement–sand ratio, and initial void ratio) on the triaxial shear behavior were investigated using discrete element method. Cemented sand samples with different initial conditions were prepared and subjected to the consolidated drained triaxial shearing test. In the simulations, the peak strength, residual strength, and pre-peak stiffness of cemented sand were enhanced by increasing the curing time and cement–sand ratio, and the enhancements could be explained by the increases in bond strength and bond number. Resulting from the increases of these two sampling factors, bond breakage emerged at a greater axial strain but lower intensity. However, some uncommon phenomena were generated; that is, the contractive but strain-softening response occurred in the sample with a curing time of 3 days, and the shear band and the strain-hardening behavior coexisted in the sample with a cement–sand ratio of 1%. The peak strength and pre-peak stiffness were also enhanced by decreasing the initial void ratio, more distinctly than by increasing the curing time and cement–sand ratio. However, the residual strength, bond breakage, and failure pattern with the persistence of shear band were insensitive to this change.

Direct Compaction Drug Product Process Modeling

Most challenges during the development of solid dosage forms are related to the impact of any variations in raw material properties, batch size, or equipment scales on the product quality and the control of the manufacturing process. With the ever pertinent restrictions on time and resource availability versus heightened expectations to develop, optimize, and troubleshoot manufacturing processes, targeted and robust science-based process modeling platforms are essential. This review focuses on the modeling of unit operations and practices involved in batch manufacturing of solid dosage forms by direct compaction. An effort is made to highlight the key advances in the past five years, and to propose potentially beneficial future study directions.

Recent advances in positron emission particle tracking: a comparative review

Positron emission particle tracking (PEPT) is a technique which allows the high-resolution, three-dimensional imaging of particulate and multiphase systems, including systems which are large, dense, and/or optically opaque, and thus difficult to study using other methodologies. In this work, we bring together researchers from the world's foremost PEPT facilities not only to give a balanced and detailed overview and review of the technique but, for the first time, provide a rigorous, direct, quantitative assessment of the relative strengths and weaknesses of all contemporary PEPT methodologies. We provide detailed explanations of the methodologies explored, including also interactive code examples allowing the reader to actively explore, edit and apply the algorithms discussed. The suite of benchmarking tests performed and described within the document is made available in an open-source repository for future researchers.

Load-Balancing Strategies in Discrete Element Method Simulations

In this research, we investigate the influence of a load-balancing strategy and parametrization on the speed-up of discrete element method simulations using Lethe-DEM. Lethe-DEM is an open-source DEM code which uses a cell-based load-balancing strategy. We compare the computational performance of different cell-weighing strategies based on the number of particles per cell (linear and quadratic). We observe two minimums for particle to cell weights (at 3, 40 for quadratic, and 15, 50 for linear) in both linear and quadratic strategies. The first and second minimums are attributed to the suitable distribution of cell-based and particle-based functions, respectively. We use four benchmark simulations (packing, rotating drum, silo, and V blender) to investigate the computational performances of different load-balancing schemes (namely, single-step, frequent and dynamic). These benchmarks are chosen to demonstrate different scenarios that may occur in a DEM simulation. In a large-scale rotating drum simulation, which shows the systems in which particles occupy a constant region after reaching steady-state, single-step load-balancing shows the best performance. In a silo and V blender, where particles move in one direction or have a reciprocating motion, frequent and dynamic schemes are preferred. We propose an automatic load-balancing scheme (dynamic) that finds the best load-balancing steps according to the imbalance of computational load between the processes. Furthermore, we show the high computational performance of Lethe-DEM in the simulation of the packing of 108 particles on 4800 processes. We show that simulations with optimum load-balancing need ≈40% less time compared to the simulations with no load-balancing.

Highlighting DEM's Potential to Gauge Mechanistic Attributes of MUPS Tablet and Capsule Formulations

Simulation of pharmaceutical unit operations by the discrete element method (DEM) has elevated our understanding of the impact of single-particle interactions on themselves, and on the entire tablets/powder. Studies in the past have shown how this knowledge helps to mitigate/solve multiple challenges during conventional formulation and process development/modernization/troubleshooting, with minimal use of active drug material. This communication adds to this- highlighting the tool's potential for a rapid preliminary assessment of the mechanistic attributes of multiple unit particle system (MUPS) based tablet and capsule drug products.

Computational Modeling of Fluidized Beds with a Focus on Pharmaceutical Applications: A Review

The fluidized bed is an essential and standard equipment in the field of process development. It has a wide application in various areas and has been extensively studied. This review paper aims to discuss computational modeling of a fluidized bed with a focus on pharmaceutical applications. Eulerian, Lagrangian, and combined Eulerian-Lagrangian models have been studied for fluid bed applications with the rise of modeling capabilities. Such models assist in optimizing the process parameters and expedite the process development cycle. This paper discusses the background of modeling and then summarizes research papers relevant to pharmaceutical unit operations.

Process Modeling and Simulation of Tableting-An Agent-Based Simulation Methodology for Direct Compression

In pharmaceutical manufacturing, the utmost aim is reliably producing high quality products. Simulation approaches allow virtual experiments of processes in the planning phase and the implementation of digital twins in operation. The industrial processing of active pharmaceutical ingredients (APIs) into tablets requires the combination of discrete and continuous sub-processes with complex interdependencies regarding the material structures and characteristics. The API and excipients are mixed, granulated if required, and subsequently tableted. Thereby, the structure as well as the properties of the intermediate and final product are influenced by the raw materials, the parametrized processes and environmental conditions, which are subject to certain fluctuations. In this study, for the first time, an agent-based simulation model is presented, which enables the prediction, tracking, and tracing of resulting structures and properties of the intermediates of an industrial tableting process. Therefore, the methodology for the identification and development of product and process agents in an agent-based simulation is shown. Implemented physical models describe the impact of process parameters on material structures. The tablet production with a pilot scale rotary press is experimentally characterized to provide calibration and validation data. Finally, the simulation results, predicting the final structures, are compared to the experimental data.

Predictive Approach to Understand and Eliminate Tablet Breakage During Film Coating

Pharmaceutical tablets can be susceptible to damage such as edge chipping or erosion of the core during the tablet coating process. The intersection of certain process parameters, equipment design, and tablet properties may induce more significant tablet damage such as complete tablet fracture. In this work, a hybrid predictive approach was developed using discrete element method (DEM) modeling and lab-based tablet impact experiments to identify conditions that may lead to tablet breakage events. The approach was extended to examine potential modifications to the coating equipment and process conditions in silico to mitigate the likelihood of tablet breakage during future batches. The approach is shown to enhance process understanding, identify optimal process conditions within development constraints, and de-risk the manufacture of future tablet coating batches.

Hybrid multi-zonal compartment modeling for continuous powder blending processes

To modernize drug manufacturing, the pharmaceutical industry has been moving towards implementing emerging technologies to enhance manufacturing robustness and process reliability for production of regulation compliant drug products. Although different science and risk based technologies, like Quality-by-Design, have been used to illustrate their potential, there still exist some underlying obstacles. Specifically, for the production of oral solid drug products, an in-depth process understanding, and predictive modeling of powder mixing in continuous powder blenders is one such major obstacle and originates from the current limitations of the experimental and modeling approaches. Though first principle based discrete element modeling (DEM) approach can address the above issues, it can get very computationally intensive which limits its applications for predictive modeling. In the proposed work, we aim to address this limitation using a multi-zonal compartment modeling approach, which is constructed from DEM. The approach provides a computationally efficient and mechanistically informed hybrid model. The application of the proposed approach is first demonstrated for a periodic section of the blender, followed by its extension for the entire continuous powder blender and the obtained model predictions are validated. The proposed approach provides an overall assessment of powder mixing along axial and radial directions, which is an important requirement for the quantification of blend uniformity. Given the low computational cost, the developed model can further be integrated within the predictive flowsheet model of the manufacturing line.

High-throughput blend segregation evaluation using automated powder dispensing technology

Due to the complexity in the interactions of variables and mechanisms leading to blend segregation, quantifying the segregation propensity of an Active Pharmaceutical Ingredient (API) has been challenging. A high-throughput segregation risk prediction workflow for early drug product development has been developed based on the dispensing mechanism of automated powder dispensing technology. The workflow utilized liquid handling robots and high-performance liquid chromatography (HPLC) with a well-plate autosampler for sample preparation and analysis. Blends containing three different APIs of varying concentrations and particle sizes of different constituents were evaluated through this automated workflow. The workflow enabled segregation evaluation of different API blends in very small quantities (~7g) compared to other common segregation testers that consume hundreds of grams. Segregation patterns obtained were well explained with vibration induced percolation-based segregation phenomena. Segregation risk was translated quantitatively using relative standard deviation (RSD) calculations, and the results matched well with large-scale segregation studies. The applied approach increased the throughput, introduced a simple and clean walk-up method with minimized equipment space and API exposures to conduct segregation studies. Results obtained can provide insights about optimizing particle size distributions, as well as selecting appropriate formulation constituents and secondary processing steps in early drug product development when the amount of available API is very limited.

Artificial intelligence in drug discovery and development

Artificial intelligence-integrated drug discovery and development has accelerated the growth of the pharmaceutical sector, leading to a revolutionary change in the pharma industry. Here, we discuss areas of integration, tools, and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them.

An investigation of collisions of liquid coated particles

The presence of even small amounts of liquid between particles dramatically changes the dynamics of collisions. This study considers granular collisions between two particles coated with a thin layer of viscous liquid, such that the capillary number is high and viscous forces dominate. High-speed particle tracking velocimetry was used to experimentally study the collisions of two smooth spheres with and without liquid coatings. We then use these experiments to examine four theoretical models that describe the collisions. A key challenge when modelling viscous forces is that the force which is predicted as particles approach each other scales with the inverse of the distance, i.e. tends to infinity. Most existing models impose a limit to the viscous force. One recent model instead assumes a hard sphere collision. These fundamentally different approaches produce different rebound outcomes. A fair match between experiments and simulations was obtained when using the hard sphere collision model, but only if an empirically-fitted glass transition pressure model was used to describe the minimum approach distance.

Underpinning mechanistic understanding of the segregation phenomena of pharmaceutical blends using a near-infrared (NIR) spectrometer embedded segregation tester

The segregation of an active pharmaceutical ingredient (API) within a powder blend is one of the major manufacturing obstacles in achieving content uniformity. Segregation can be due to differences in physicochemical properties of formulation components and/or perturbations experienced during secondary processing steps, such as granulation, fluidization, die-filling and compression. A near-infrared (NIR) spectrometer embedded segregation tester, which could mimic the external stimulations (vibration and fluidization) experienced by a blend in a manufacturing facility, was used to evaluate and predict blend segregation. Two different GlaxoSmithKline (GSK) product blends with variations in the API particle size and concentration were tested. Drug content was further measured at different locations along the powder bed by NIR to sketch the segregation profile and calculate the overall segregation intensity of each blend. The study indicated that the segregation potential was dependent on the particle sizes of API and excipients, as well as the type of stimulus applied (vibration vs fluidization). Drug concentration profiles obtained from this mode of analysis decoded the underlying segregation mechanisms (sieving, trajectory and air elutriation) easily. The employed NIR-based segregation tester proved to be a useful small-scale predictive tool to evaluate and rank the segregation risk of the studied pharmaceutical blends.

Impact of Insoluble Separation Layer Mechanical Properties on Disintegration and Dissolution Kinetics of Multilayer Tablets

Dissolution and disintegration of solid dosage forms such as multiple-layer tablet with different active ingredients depend on formulation and properties used in the formulations, and it may sometimes result in counterintuitive release kinetics. In this manuscript, we investigate the behavior of combined acetylsalicylic acid and mefenamic acid bi- and triple-layer formulations. We show that the simulation model with a cellular automata predicted the impact of the inert layer between the different active ingredients on each drug release and provide a good agreement with the experimental results. Also, it is shown that the analysis based on the Noyes–Whitney equation in combination with a cellular automata-supported dissolution and disintegration numerical solutions explain the nature of the unexpected effects. We conclude that the proposed simulation approach is valuable to predict the influence of material attributes and process parameters on drug release from multicomponent and multiple-layer pharmaceutical tablets and to help us develop the drug product formulation.

Discrete element modeling for continuous powder feeding operation: Calibration and system analysis

Research emphases on extensive experimental studies and modeling efforts have been on the rise for the development of accurate predictive models of pharmaceutical unit operations and 'digital-twin' framework for continuous manufacturing lines. These exhaustive studies have been conducted at different process conditions to acquire comprehensive knowledge of effects of process parameters on the overall process dynamics. However, there still lacks a detailed understanding of material property effects of pharmaceutical powders on process operation. To address this issue, a discrete element modeling (DEM) approach combined with material calibration is applied for simulation of feeder unit to obtain particle-level insight into effects of material properties on feeder performance with focus on particle flow and powder mixing within the feeder unit. Bulk calibration is implemented to accurately represent powder material properties within the DEM framework. Different refill situations are simulated using DEM to observe powder mixing, measured at the outlet. Feeder DEM simulations are further applied to understand correlations of material properties on feeder operation. These studies provide a detailed physical insight and particle-scale information into the powder mechanics during powder feeding operation.

A Coarse Grained Model for Viscoelastic Solids in Discrete Multiphysics Simulations

Viscoelastic bonds intended for Discrete Multiphysics (DMP) models are developed to allow the study of viscoelastic particles with arbitrary shape and mechanical inhomogeneity that are relevant to the pharmaceutical sector and that have not been addressed by the Discrete Element Method (DEM). The model is applied to encapsulate particles with a soft outer shell due, for example, to the partial ingress of moisture. This was validated by the simulation of spherical homogeneous linear elastic and viscoelastic particles. The method is based on forming a particle from an assembly of beads connected by springs or springs and dashpots that allow the sub-surface stress fields to be computed, and hence an accurate description of the gross deformation. It is computationally more expensive than DEM, but could be used to define more effective interaction laws.

Compaction of elastic granular materials: inter-particles friction effects and plastic events

The uni-axial compaction of granular materials made of elastic neo-Hookean particles is investigated in the quasi-static regime. Two-dimensional disk assemblies are simulated using the Finite Element model coupled with Contact Dynamics method for dealing both with finite deformations of the particles and contact interactions. Due to large deformations of the particles, the packing fraction of the system increases continuously during the compaction process, reaching values close to 1. The influence of the coefficient of friction between the particles on the macroscopic and micro-structural behaviors of the system is thoroughly discussed.

Application of the Discrete Element Method for Manufacturing Process Simulation in the Pharmaceutical Industry

Process simulation using mathematical modeling tools is becoming more common in the pharmaceutical industry. A mechanistic model is a mathematical modeling tool that can enhance process understanding, reduce experimentation cost and improve product quality. A commonly used mechanistic modeling approach for powder is the discrete element method (DEM). Most pharmaceutical materials have powder or granular material. Therefore, DEM might be widely applied in the pharmaceutical industry. This review focused on the basic elements of DEM and its implementations in pharmaceutical manufacturing simulation. Contact models and input parameters are essential elements in DEM simulation. Contact models computed contact forces acting on the particle-particle and particle-geometry interactions. Input parameters were divided into two types-material properties and interaction parameters. Various calibration methods were presented to define the interaction parameters of pharmaceutical materials. Several applications of DEM simulation in pharmaceutical manufacturing processes, such as milling, blending, granulation and coating, were categorized and summarized. Based on this review, DEM simulation might provide a systematic process understanding and process control to ensure the quality of a drug product.

Influence of particle size and blender size on blending performance of bi-component granular mixing: A DEM and experimental study

The effect of particle size enlargement and blender geometry down-scaling on the blend uniformity (BU) was evaluated by Discrete Element Method (DEM) to predict the blending performance of a binary granular mixture. Three 10 kg blending experiments differentiated by the physical properties specifically particle size were performed as reference for DEM simulations. The segregation behavior observed during the diffusion blending was common for all blends, while the sample BU, i.e., standard deviation of active ingredient content % was different among the three blends reflecting segregation due to the particle size differences between the components. Quantitative prediction of the sample BU probability density distribution in reality based on the DEM simulation results was successfully demonstrated. The average root mean square error normalized by the mean of the mean sample BU in the blends was 0.228. Beside the ratio of blender container to particle size, total number of particles in the blender and the number of particles in a sample were confirmed critical for the blending performance. These in-silico experiments through DEM simulations would help in setting a design space with respect to the particle size and in a broader sense with respect to the physical properties in general.

Scale-Up Strategy in Quality by Design Approach for Pharmaceutical Blending Process with Discrete Element Method Simulation

An approach combining quality by design (QbD) and the discrete element method (DEM) is proposed to establish an effective scale-up strategy for the blending process of an amlodipine formulation prepared by the direct compression method. Critical process parameters (CPPs) for intermediate critical quality attributes (IQAs) were identified using risk assessment (RA) in the QbD approach. A Box–Behnken design was applied to obtain the operating space for a laboratory-scale. A DEM model was developed by the input parameters for the amlodipine formulation; blending was simulated on a laboratory-scale V-blender (3 L) at optimal settings. The efficacy and reliability of the DEM model was validated through a comparison of simulation and experimental results. Change of operating space was evaluated using the validated DEM model when scaled-up to pilot-scale (10 L). Pilot-scale blending was simulated on a V-blender and double-cone blender at the optimal settings derived from the laboratory-scale operating space. Both pilot-scale simulation results suggest that blending time should be lower than the laboratory-scale optimized blending time to meet target values. These results confirm the change of operating space during the scale-up process. Therefore, this study suggests that a QbD-integrated DEM simulation can be a desirable approach for an effective scale-up strategy.

Calibration of Discrete-Element-Method Parameters for Cohesive Materials Using Dynamic-Yield-Strength and Shear-Cell Experiments

This study tested the effectiveness of using dynamic yield strength (DYS) and shear-cell experiments to calibrate the following discrete-element-method (DEM) parameters: surface energy, and the coefficients of sliding and rolling friction. These experiments were carried out on cohesive granules, and DEM models were developed for these experiment setups using the JKR cohesion contact model. Parameter-sensitivity analysis on the DYS model showed that the DYS results in the simulations were highly sensitive to surface energy and were also impacted by the values of the two friction coefficients. These results indicated that the DYS model could be used to calibrate the surface energy parameter once the friction coefficients were fixed. Shear-cell sensitivity analysis study found that the influence of surface energy on the critical-state shear value cannot be neglected. It was inferred that the shear-cell model has to be used together with the DYS model to identify the right set of friction parameters. Next, surface energy was calibrated using DYS simulations for a chosen set of friction parameters. Calibrations were successfully conducted for simulations involving experimentally sized particles, scaled-up particles, a different shear modulus, and a different set of friction parameters. In all these cases, the simulation DYS results were found to be linearly correlated with surface energy and were within 5% of the experimental DYS result. Shear-cell simulations were then used to compare calibrated surface-energy values for the scaled-up particles with the experimentally sized particles. Both the simulations resulted in similar critical-state shear values. Finally, it was demonstrated that a combination of DYS and shear-cell simulations could be used to compare two sets of friction parameters and their corresponding calibrated surface energy values to identify the set of parameters that better represent the flow behavior demonstrated by the experimental system.